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研究生:佟紹鵬
研究生(外文):Shao-Peng, Tung
論文名稱:基於深度學習之工業用智慧型機器視覺系統:以焊點品質檢測為例
論文名稱(外文):An Industrial AI Vision System based on Deep Learning: an example of solder joint quality inspection
指導教授:栗永徽
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:53
中文關鍵詞:深度學習工業檢測焊點焊接
外文關鍵詞:deep learningindustrial inspectionsolder jointsoldering
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焊點是電子元件和電路板的相會之處,良好的焊接可以讓電路正常運作,然而有瑕疵的焊接會讓整個電路產生不可預期的錯誤,因此焊點的品質對產品的成敗有直接的關係。
過去的自動化視覺檢測系統仰賴人所制定的規則(rule-based),其修正過程充滿不確定性,本研究通過深度學習以訓練智慧型機器視覺系統,使其能夠辨識焊點的好壞。
Xception是google繼Inception架構而生的神經網路架構,本論文使用Xception對焊點進行訓練。利用合格與品質不良的焊點樣本經過卷積之後所產生的特徵圖之差異訓練智慧型機器視覺系統,使該系統能夠分辨出焊點之良莠。
Solder joints are the intersection of electronic components and circuit boards. Good soldering allows the circuit to operate normally. However, flawed soldering can cause unpredictable errors in the entire circuit. Therefore, the quality of solder joints has a direct impact on the quality of the electronic product.
In the past, the automated visual inspection system usually functions in rule-based fashion, and the fine-tuning process was full of uncertainty. In this study we apply deep learning paradigm to train the neural network model to identify the quality of the solder joints.
Xception is a neural network architecture which inherits the concept of Inception created by Google. This paper uses solder joints to train Xception. A neural network model trained with the difference between the feature maps produced by the convolution of Pass and Ng solder joint samples can identify the quality of the solder joints.
摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 研究背景與動機 1
1.1 研究背景與動機 1
1.2 工業用自動化光學檢察系統(AOI)與影像分類 2
1.3 文獻回顧 3
第二章 焊點品質之好壞及瑕疵之分類 5
2.1 焊點分類 5
2.1.1 合格焊點(PASS) 6
2.1.2 包焊(NG) 7
2.1.3 虛焊(NG) 8
2.1.4 空焊(NG) 9
2.1.5 連錫(NG) 10
第三章 實驗方法 11
3.1 生成式對抗網路 Generative Adversarial Networks(GAN) 12
3.2 Xception 16
第四章 實驗結果及討論 25
4.1 資料收集 25
4.2 資料前處理 26
4.3 資料集 27
4.4 結果 27
4.5 討論 32
4.6 PCB瑕疵監測系統畫面 34
第五章 結論 36
REFERENCES 38
REFERENCES

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